RAG / Vector Database Implementation
RAG Isn't "Embed and Pray." Retrieval quality is the whole game. We engineer RAG systems that pull the right context, ground every answer, and prove their accuracy with evaluation — not hope.
Partnering with Leading Brands Across the Globe
Trusted by leading brands worldwide, we deliver scalable digital solutions that drive innovation, performance, and measurable business impact.
Your Data Is Trapped. Generic AI Is a Liability.
Your most valuable knowledge sits in documents, tickets, and contracts a generic LLM can't see — and bolt one on carelessly and it invents answers or retrieves the wrong passage with total confidence. A demo that works on three clean PDFs is not a system that works on a hundred thousand messy ones. We build RAG the way production demands: real ingestion for complex documents, a deliberate chunking and embedding strategy, the right vector database, hybrid search with reranking, and grounding with citations — then we measure retrieval quality and answer accuracy. Embedding is easy; retrieval quality is the asset.
What We Engineer in RAG & Vector DB
RAG readiness and data assessment
what's retrievable, what's missing, what it'll take.
Ingestion and ETL pipelines
complex PDFs, tables, images, and structured sources.
Chunking and embedding strategy
tuned for your content, not a generic default.
Vector database selection and architecture
Pinecone, Weaviate, Milvus, or pgvector, chosen on merit.
Hybrid and semantic search with reranking
precision retrieval, not keyword luck.
Grounding, citations, and guardrails
verifiable answers, controlled behavior.
Evaluation and hallucination monitoring
the asset that proves the system is accurate.
Private and self-hosted LLM deployment
keep sensitive data in your environment.
Agentic and multi-modal RAG
tool use, multi-step reasoning, and image/document inputs.
The Numbers Behind Our Engineering
How We Deliver
Assess
review your content, use case, and accuracy and security requirements.
Architect
design the ingestion, embedding, vector store, and retrieval strategy.
Build
implement the pipeline, search, grounding, and guardrails.
Evaluate
measure retrieval quality and answer accuracy; tune until it holds.
Deploy and operate
ship securely, then monitor for drift and hallucination.
Wondering whether RAG or fine-tuning fits your case? We'll give you a straight answer in a free 30-minute session.
Book a free RAG consultationIndustries We Cover
Healthcare & Life Sciences
Grounded answers over clinical guidelines and records, kept private.
Banking, Financial Services & Insurance
Accurate retrieval over policies, contracts, and compliance documents.
Legal & Compliance
Cited answers from contracts, case law, and regulations.
Retail & eCommerce
Product and support knowledge assistants that don't hallucinate.
Manufacturing
Searchable manuals, SOPs, and maintenance histories.
Public Sector
Secure, self-hosted knowledge AI for sensitive records.
Case Studies That Prove It
Ways to Work With Us
Fixed-scope project
a defined RAG system or vector-search build, priced up front.
Dedicated AI pod
an embedded team for ongoing RAG and knowledge-AI work.
Staff augmentation
senior RAG and data engineers inside your team.
RAG audit and evaluation
an independent read on your retrieval quality and hallucination rate.
Our RAG & Vector DB Stack
Frameworks
LangChain · LlamaIndex · Python
Vector databases
Pinecone · Weaviate · Milvus · pgvector · FAISS
Models
GPT · Llama · Mistral · Hugging Face embeddings
Infrastructure
Docker · Kubernetes · AWS · Azure
Why Teams Choose QSS for RAG
Retrieval quality is our obsession
Right context in, grounded answers out.
Evaluation-driven
We measure accuracy and hallucination, not just ship a demo.
Secure by design
Private and self-hosted options; ISO 27001 and CMMI Level 5 delivery.
Database-agnostic
We pick the vector store that fits you, not our preferences.
Real production RAG
Pinecone and FAISS systems already live for clients.
You own the data and IP
Embeddings, pipelines, and code are yours.
Frequently Asked Questions
It depends on data complexity, volume, and security needs. A focused knowledge assistant is far smaller than an enterprise, multi-source system. We scope and price up front, with no open-ended billing.
A focused RAG system typically reaches a working, evaluated state in weeks; complex or multi-source builds are phased so you see value early.
RAG is usually the better first move when answers must reflect your current, proprietary knowledge — faster, cheaper, and easy to keep updated. Fine-tuning suits consistent style or behavior. Often the best system uses both, and we advise honestly.
Yes. We support private and self-hosted LLM and vector-store deployments so sensitive data never leaves your environment.
Completely. All data, embeddings, and IP are yours on delivery.
With a free RAG consultation and readiness assessment, followed by a scoped plan and timeline.
Let's Build RAG That's Actually Accurate.
Tell us what knowledge you want your AI to use, and we'll design retrieval you can trust.
Book a free RAG consultation